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https://github.com/qurator-spk/sbb_binarization.git
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issue #45 the patches option is omitted and it means that documents will be processed in patches while no patches is not desired by the tool
This commit is contained in:
vahid
parent
eff7a47852
commit
7c3f2176f7
3 changed files with 125 additions and 142 deletions
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@ -7,9 +7,8 @@ from .sbb_binarize import SbbBinarizer
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@command()
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@command()
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@version_option()
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@version_option()
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@option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
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@option('--model-dir', '-m', type=types.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
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@option('--model-dir', '-m', type=types.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
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@argument('input_image')
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@argument('input_image')
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@argument('output_image')
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@argument('output_image')
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def main(patches, model_dir, input_image, output_image):
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def main(model_dir, input_image, output_image):
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SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image)
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SbbBinarizer(model_dir).run(image_path=input_image, save=output_image)
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@ -110,7 +110,7 @@ class SbbBinarizeProcessor(Processor):
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if oplevel == 'page':
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if oplevel == 'page':
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LOG.info("Binarizing on 'page' level in page '%s'", page_id)
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LOG.info("Binarizing on 'page' level in page '%s'", page_id)
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bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image), use_patches=True))
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bin_image = cv2pil(self.binarizer.run(image=pil2cv(page_image)))
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# update METS (add the image file):
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# update METS (add the image file):
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bin_image_path = self.workspace.save_image_file(bin_image,
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bin_image_path = self.workspace.save_image_file(bin_image,
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file_id + '.IMG-BIN',
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file_id + '.IMG-BIN',
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@ -124,7 +124,7 @@ class SbbBinarizeProcessor(Processor):
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LOG.warning("Page '%s' contains no text/table regions", page_id)
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LOG.warning("Page '%s' contains no text/table regions", page_id)
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for region in regions:
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for region in regions:
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region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh, feature_filter='binarized')
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region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh, feature_filter='binarized')
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region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image), use_patches=True))
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region_image_bin = cv2pil(binarizer.run(image=pil2cv(region_image)))
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region_image_bin_path = self.workspace.save_image_file(
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region_image_bin_path = self.workspace.save_image_file(
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region_image_bin,
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region_image_bin,
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"%s_%s.IMG-BIN" % (file_id, region.id),
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"%s_%s.IMG-BIN" % (file_id, region.id),
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@ -139,7 +139,7 @@ class SbbBinarizeProcessor(Processor):
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LOG.warning("Page '%s' contains no text lines", page_id)
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LOG.warning("Page '%s' contains no text lines", page_id)
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for region_id, line in region_line_tuples:
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for region_id, line in region_line_tuples:
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line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized')
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line_image, line_xywh = self.workspace.image_from_segment(line, page_image, page_xywh, feature_filter='binarized')
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line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image), use_patches=True))
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line_image_bin = cv2pil(binarizer.run(image=pil2cv(line_image)))
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line_image_bin_path = self.workspace.save_image_file(
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line_image_bin_path = self.workspace.save_image_file(
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line_image_bin,
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line_image_bin,
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"%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id),
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"%s_%s_%s.IMG-BIN" % (file_id, region_id, line.id),
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@ -62,7 +62,7 @@ class SbbBinarizer:
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n_classes = model.layers[len(model.layers)-1].output_shape[3]
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n_classes = model.layers[len(model.layers)-1].output_shape[3]
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return model, model_height, model_width, n_classes
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return model, model_height, model_width, n_classes
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def predict(self, model_in, img, use_patches):
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def predict(self, model_in, img):
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tensorflow_backend.set_session(self.session)
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tensorflow_backend.set_session(self.session)
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model, model_height, model_width, n_classes = model_in
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model, model_height, model_width, n_classes = model_in
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@ -102,151 +102,135 @@ class SbbBinarizer:
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img = np.copy(img_padded)
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img = np.copy(img_padded)
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margin = int(0.1 * model_width)
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width_mid = model_width - 2 * margin
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height_mid = model_height - 2 * margin
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if use_patches:
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img = img / float(255.0)
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margin = int(0.1 * model_width)
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img_h = img.shape[0]
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img_w = img.shape[1]
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width_mid = model_width - 2 * margin
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prediction_true = np.zeros((img_h, img_w, 3))
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height_mid = model_height - 2 * margin
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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img = img / float(255.0)
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img_h = img.shape[0]
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img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - model_width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - model_height
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i == 0 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i != 0 and i != nxf-1 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
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elif i != 0 and i != nxf-1 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color
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else:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
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prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
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prediction_true = prediction_true.astype(np.uint8)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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else:
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img_h_page = img.shape[0]
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nxf = int(nxf)
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img_w_page = img.shape[1]
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img = img / float(255.0)
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img = resize_image(img, model_height, model_width)
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label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - model_width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - model_height
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i == 0 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
|
||||||
|
|
||||||
|
elif i == nxf-1 and j != 0 and j != nyf-1:
|
||||||
|
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
|
||||||
|
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
|
||||||
|
|
||||||
|
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
|
||||||
|
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
|
||||||
|
|
||||||
|
elif i != 0 and i != nxf-1 and j == 0:
|
||||||
|
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
|
||||||
|
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
|
||||||
|
|
||||||
|
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
|
||||||
|
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
|
||||||
|
|
||||||
|
elif i != 0 and i != nxf-1 and j == nyf-1:
|
||||||
|
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
|
||||||
|
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
|
||||||
|
|
||||||
|
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
|
||||||
|
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color
|
||||||
|
|
||||||
|
else:
|
||||||
|
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
|
||||||
|
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
|
||||||
|
|
||||||
|
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
|
||||||
|
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
|
||||||
|
prediction_true = prediction_true.astype(np.uint8)
|
||||||
|
|
||||||
seg = np.argmax(label_p_pred, axis=3)[0]
|
|
||||||
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
|
||||||
prediction_true = resize_image(seg_color, img_h_page, img_w_page)
|
|
||||||
prediction_true = prediction_true.astype(np.uint8)
|
|
||||||
return prediction_true[:,:,0]
|
return prediction_true[:,:,0]
|
||||||
|
|
||||||
def run(self, image=None, image_path=None, save=None, use_patches=False):
|
def run(self, image=None, image_path=None, save=None):
|
||||||
if (image is not None and image_path is not None) or \
|
if (image is not None and image_path is not None) or \
|
||||||
(image is None and image_path is None):
|
(image is None and image_path is None):
|
||||||
raise ValueError("Must pass either a opencv2 image or an image_path")
|
raise ValueError("Must pass either a opencv2 image or an image_path")
|
||||||
|
|
@ -256,7 +240,7 @@ class SbbBinarizer:
|
||||||
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
|
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
|
||||||
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
|
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
|
||||||
|
|
||||||
res = self.predict(model, image, use_patches)
|
res = self.predict(model, image)
|
||||||
|
|
||||||
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
|
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
|
||||||
res[:, :][res[:, :] == 0] = 2
|
res[:, :][res[:, :] == 0] = 2
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue